As natural language models like ChatGPT become increasingly prevalent in
applications and services, the need for robust and accurate methods to detect
their output is of paramount importance. In this paper, we present GPT Reddit
Dataset (GRiD), a novel Generative Pretrained Transformer (GPT)-generated text
detection dataset designed to assess the performance of detection models in
identifying generated responses from ChatGPT. The dataset consists of a diverse
collection of context-prompt pairs based on Reddit, with human-generated and
ChatGPT-generated responses. We provide an analysis of the dataset's
characteristics, including linguistic diversity, context complexity, and
response quality. To showcase the dataset's utility, we benchmark several
detection methods on it, demonstrating their efficacy in distinguishing between
human and ChatGPT-generated responses. This dataset serves as a resource for
evaluating and advancing detection techniques in the context of ChatGPT and
contributes to the ongoing efforts to ensure responsible and trustworthy
AI-driven communication on the internet. Finally, we propose GpTen, a novel
tensor-based GPT text detection method that is semi-supervised in nature since
it only has access to human-generated text and performs on par with
fully-supervised baselines.

通过 GPT Reddit 数据集（GRiD）和新颖的 GpTen 张量基准模型，本论文提出了一种对 ChatGPT 生成的文本进行检测的方法，旨在评估检测模型在识别 ChatGPT 生成应答方面的性能。该数据集包含了基于 Reddit 的多样化的语境 - 提示对，同时包括了人类生成的应答和 ChatGPT 生成的应答，通过对数据集特征进行分析，如语言多样性、上下文复杂性和应答质量，展示了这个数据集的实用性。实验中比较了几种检测方法，并展示了它们在区分人类和 ChatGPT 生成应答方面的有效性。该数据集为评估和推进 ChatGPT 的检测技术提供了资源，也为确保互联网上负责任和值得信赖的基于 AI 的交流做出了贡献。最后，我们提出了 GpTen 作为一种半监督方法来检测 GPT 生成的文本，该方法仅有人类生成的文本作为参考，并能与完全监督的基准模型表现相当。